1 research outputs found
λͺ¨μ ν리머ν°λΈλ₯Ό μ΄μ©ν 볡μ‘ν λ‘λ΄ μ무 νμ΅ λ° μΌλ°ν κΈ°λ²
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν ν곡μ°μ£Όκ³΅νκ³Ό, 2020. 8. κΉνμ§.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements.
This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset.
In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion.
This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task.
Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.μμ° νμ΅ κΈ°λ²(Learning from demonstrations, LfD)μ λ‘λ΄μ΄ νΉμ λμμ μνν μ μλλ‘ νλ μ λ§ν λμ μμ± κΈ°λ²μ΄λ€. λ‘λ΄ μ‘°μκΈ°κ° μΈκ° μ¬νμμ λ€μν μ
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Όλ¬Έμ LfD κΈ°λ² μ€ λͺ¨μ
ν리머ν°λΈ κΈ°λ°μ λμ μ¬μμ± μκ³ λ¦¬μ¦μΈ Parametric dynamic movement primitives(PDMP)μ κΈ°μ΄ν μκ³ λ¦¬μ¦μ μ μνλ©°, μ΄λ₯Ό ν΅ν΄ λ€μν μ무λ₯Ό μννλ λͺ¨λ°μΌ μ‘°μκΈ°μ κΆ€μ μ μμ±νλ€. κΈ°μ‘΄μ λμ μ¬μμ± μκ³ λ¦¬μ¦κ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ μ 곡λ μμ°μμ ννλ λμμ λ¨μν μ¬μμ±νλ κ²μ κ·ΈμΉμ§ μκ³ , μλ‘μ΄ νκ²½μ λ§κ² μΌλ°ν νλ κ³Όμ μ ν¬ν¨νλ€. μ΄ λ
Όλ¬Έμμ μ μνλ μΌλ°ν κ³Όμ μ PDMPsμ λ΄λΆ νλΌλ―Έν° κ°μΈ μ€νμΌ νλΌλ―Έν°μ νκ²½ λ³μ μ¬μ΄μ λΉμ ν κ΄κ³λ₯Ό κ°μ°μ€ νκ· κΈ°λ² (Gaussian process regression, GPR)μ μ΄μ©νμ¬ μμμ μΌλ‘ νννλ€. μ μλ κΈ°λ²μ λν μ΅μ μμ°λ₯Ό νμ΅νλ λ°©μμ ν΅ν΄ κ°λ ₯ν μ΅μ μ€μκ° κ²½λ‘ κ³ν κΈ°λ²μΌλ‘λ μμ©λ μ μλ€.
λ³Έ λ
Όλ¬Έμμλ λν λ‘λ΄μ ꡬλ μμ μ±λ κ³ λ €νλ€. κΈ°μ‘΄ μ°κ΅¬λ€μμ λ€λ£¨μ΄μ§ μμ° κ΄λ¦¬ κΈ°μ μ΄ λ‘λ΄μ ꡬλ ν¨μ¨μ±μ κ°μ νλ λ°©ν₯μΌλ‘ μ μλ κ²κ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ κ°ν ꡬμ쑰건μΌλ‘ λ‘λ΄μ ꡬλ μμ μ±μ ν보νλ μμ° κ΄λ¦¬ κΈ°μ μ ν΅ν΄ μμ μ±μ κ³ λ €νλ μλ‘μ΄ λ°©μμ μ μνλ€. μ μλ λ°©μμ μ€νμΌ νλΌλ―Έν° κ° μμμ μμ μ± κΈ°μ€μ κ³μ°νλ©°, μ΄ μμ κΈ°μ€μ ν΅ν΄ μμ°μ μ κ±°νλ μΌλ ¨μ μμ
μ μννλ€. λν, μ κ±°λ μμλ₯Ό μμ κΈ°μ€μ λ°λΌ μ
μ¦λ μμλ‘ λ체νμ¬ μΌλ°ν μ±λ₯μ μ νμν€μ§ μλλ‘ μμλ₯Ό κ΄λ¦¬νλ€. μ΄λ₯Ό ν΅ν΄ λ€μμ μμ° κ°κ° κ°λ³ λμ μμ μ± λΏ μλλΌ μ¨λΌμΈ λμμ μμ μ±κΉμ§ κ³ λ €ν μ μμΌλ©°, μ€μκ° λ‘λ΄ μ‘°μκΈ° μ΄μ©μ μμ μ±μ΄ ν보λ μ μλ€. μ μλ μμ μ±μ κ³ λ €ν μμ° κ΄λ¦¬ κΈ°μ μ λν νκ²½μ μ μ μ€μ μ΄ λ³κ²½λμ΄ λͺ¨λ μμ°μ κ΅μ²΄ν΄μΌ ν μ μλ μν©μμ μ¬μ©ν μ μλ μμ°λ€μ νλ³νκ³ , ν¨μ¨μ μΌλ‘ μ¬μ¬μ©νλ λ° μμ©ν μ μλ€.
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Όλ¬Έμ 볡μ‘ν μ무μμ μ μ©λ μ μλ PDMPsμ νμ₯ κΈ°λ²μΈ seg-PDMPsλ₯Ό μ μνλ€. μ΄ μ κ·Όλ°©μμ 볡μ‘ν μλ¬΄κ° μΌλ°μ μΌλ‘ 볡μκ°μ κ°λ¨ν νμ μμ
μΌλ‘ ꡬμ±λλ€κ³ κ°μ νλ€. κΈ°μ‘΄ PDMPsμ λ¬λ¦¬ seg-PDMPsλ μ 체 κΆ€μ μ νμ μμ
μ λνλ΄λ μ¬λ¬ κ°μ λ¨μ λμμΌλ‘ λΆν νκ³ , κ° λ¨μλμμ λν΄ μ¬λ¬κ°μ PDMPsλ₯Ό ꡬμ±νλ€. κ° λ¨μ λμ λ³λ‘ μμ±λ PDMPsλ ν΅ν©λ νλ μμν¬λ΄μμ λ¨κ³ κ²°μ νλ‘μΈμ€λ₯Ό ν΅ν΄ μλμ μΌλ‘ νΈμΆλλ€. κ° λ¨κ³ λ³λ‘ λ¨μ λμμ μννκΈ° μν μκ° λ° νμ λͺ©νμ μ κ°μ°μ€ 곡μ νκ·(GPR)λ₯Ό μ΄μ©ν νκ²½λ³μμμμ κ΄κ³μμ ν΅ν΄ μ»λλ€. κ²°κ³Όμ μΌλ‘, μ΄ μ°κ΅¬λ μ 체μ μΌλ‘ μꡬλλ μμ°μ μλ₯Ό ν¨κ³Όμ μΌλ‘ μ€μΌ λΏ μλλΌ, κ° λ¨μλμμ νν μ±λ₯μ κ°μ νλ€.
μ μλ μκ³ λ¦¬μ¦μ νλ λͺ¨λ°μΌ λ‘λ΄ μ‘°μκΈ° μ€νμ ν΅νμ¬ κ²μ¦λλ€. μΈ κ°μ§μ μλ리μ€κ° λ³Έ λ
Όλ¬Έμμ λ€λ£¨μ΄μ§λ©°, ν곡 μ΄μ‘κ³Ό κ΄λ ¨λ 첫 λ κ°μ§ μλ리μ€λ PDMPs κΈ°λ²μ΄ λ‘λ΄ μ‘°μκΈ°μμ λΉ λ₯Έ μ μμ±, μ무 ν¨μ¨μ±κ³Ό μμ μ± λͺ¨λ λ§μ‘±νλ κ²μ μ
μ¦νλ€. λ§μ§λ§ μλ리μ€λ μ§μ μ°¨λμ μ΄μ©ν λ κ°μ λ‘λ΄ μ‘°μκΈ°μ λν μ€νμΌλ‘ 볡μ‘ν μ무 μνμ νκΈ° μν΄ νμ₯λ κΈ°λ²μΈ seg-PDMPsκ° ν¨κ³Όμ μΌλ‘ λ³ννλ νκ²½μμ μΌλ°νλ λμμ μμ±ν¨μ κ²μ¦νλ€.1 Introduction 1
1.1 Motivations 1
1.2 Literature Survey 3
1.2.1 Conventional Motion Planning in Mobile Manipulations 3
1.2.2 Motion Representation Algorithms 5
1.2.3 Safety-guaranteed Motion Representation Algorithms 7
1.3 Research Objectives and Contributions 7
1.3.1 Motion Generalization in Motion Representation Algorithm 9
1.3.2 Motion Generalization with Safety Guarantee 9
1.3.3 Motion Generalization for Complex Missions 10
1.4 Thesis Organization 11
2 Background 12
2.1 DMPs 12
2.2 Mobile Manipulation Systems 13
2.2.1 Single Mobile Manipulation 14
2.2.2 Cooperative Mobile Manipulations 14
2.3 Experimental Setup 17
2.3.1 Test-beds for Aerial Manipulators 17
2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17
3 Motion Generalization in Motion Representation Algorithm 22
3.1 Parametric Dynamic Movement Primitives 22
3.2 Generalization Process in PDMPs 26
3.2.1 Environmental Parameters 26
3.2.2 Mapping Function 26
3.3 Simulation Results 29
3.3.1 Two-dimensional Hurdling Motion 29
3.3.2 Cooperative Aerial Transportation 30
4 Motion Generalization with Safety Guarantee 36
4.1 Safety Criterion in Style Parameter 36
4.2 Demonstration Management 39
4.3 Simulation Validation 42
4.3.1 Two-dimensional Hurdling Motion 46
4.3.2 Cooperative Aerial Transportation 47
5 Motion Generalization for Complex Missions 51
5.1 Overall Structure of Seg-PDMPs 51
5.2 Motion Segments 53
5.3 Phase-decision Process 54
5.4 Seg-PDMPs for Single Phase 54
5.5 Simulation Results 55
5.5.1 Initial/terminal Offsets 56
5.5.2 Style Generalization 59
5.5.3 Recombination 61
6 Experimental Validation and Results 63
6.1 Cooperative Aerial Transportation 63
6.2 Cooperative Mobile Hang-dry Mission 70
6.2.1 Demonstrations 70
6.2.2 Simulation Validation 72
6.2.3 Experimental Results 78
7 Conclusions 82
Abstract (in Korean) 93Docto